Differentiating progressive from nonprogressive T1 bladder cancer by gene expression profiling: applying RNA-sequencing analysis on archived specimens

Exploring the Relationship between CLPTM1L-MS2 Variants and Susceptibility to Bladder Cancer

CLPTM1L (Cleft Lip and Palate Transmembrane Protein 1-Like) has previously been implicated in tumorigenesis and drug resistance in cancer. However, the genetic link between CLPTM1L and bladder cancer remains uncertain. In this study, we investigated the genetic association of variable number of tandem repeats (VNTR; minisatellites, MS) regions within CLPTM1L with bladder cancer. We identified four CLPTM1L-MS regions (MS1~MS4) located in intron regions. To evaluate the VNTR polymorphic alleles, we analyzed 441 cancer-free controls and 181 bladder cancer patients. Our analysis revealed a higher frequency of specific repeat sizes within the MS2 region in bladder cancer cases compared to controls. Notably, 25 and 27 repeats were exclusively present in the bladder cancer group. Moreover, rare alleles within the medium-length repeat range (25-29 repeats) were associated with an elevated bladder cancer risk (odds ratio [OR] = 5.78, 95% confidence interval [CI]: 1.49-22.47, p = 0.004). We confirmed that all MS regions followed Mendelian inheritance, and demonstrated that MS2 alleles increased CLPTM1L promoter activity in the UM-UC3 bladder cancer cells through a luciferase assay. Our findings propose the utility of CLPTM1L-MS regions as DNA typing markers, particularly highlighting the potential of middle-length rare alleles within CLPTM1L-MS2 as predictive markers for bladder cancer risk.

SOX9/NFIA promotes human ovarian cancer metastasis through the Wnt/β-catenin signaling pathway

To our knowledge, Sex-determining Region Y box 9 (SOX9) has been in connection with a wide range of human cancers. Nevertheless, there remains uncertainty regarding SOX9's role in metastasizing ovarian cancer. In our study, SOX9 was investigated in relation to tumor metastasis in ovarian cancer as well as its potential molecular mechanisms. First, we exhibited an apparent higher expression of SOX9 in ovarian cancer tissues and cells than in normative ones, and the prognosis of patients whose SOX9 levels were high was markedly lower than that of patients whose SOX9 levels were low. Besides, highly expressed SOX9 was correlated with high grade serous carcinoma, poor tumor differentiation, high serum CA125 and lymph node metastasis. Second, SOX9 knockdown exhibited striking inhibition of the migration and invasive ability of ovarian cancer cells, whereas SOX9 overexpression had an inverse role. At the same time, SOX9 could promote ovarian cancer intraperitoneal metastasis in a nude mice in the vivo. In a similar way, SOX9 knockdown dramatically decreased the expression of nuclear factor I-A (NFIA), β-catenin as well as N-cadherin but had an increased in E-cadherin expression, as opposed to the results when SOX9 was overexpressed. Furthermore, NFIA silencing inhibited the expression of NFIA, β-catenin and N-cadherin, in the same way that E-cadherin expression was promoted. In conclusion, this study shows that SOX9 has a promotional effect on human ovarian cancer and that SOX9 promotes the metastasis of tumors by upregulating NFIA and activating on a Wnt/β-catenin signal pathway. SOX9 could be a novel focus for earlier diagnosis, therapy and prospective evaluation in ovarian cancer.

Regularized Weighted Nonparametric Likelihood Approach for High-Dimension Sparse Subdistribution Hazards Model for Competing Risk Data

Variable selection and penalized regression models in high-dimension settings have become an increasingly important topic in many disciplines. For instance, omics data are generated in biomedical researches that may be associated with survival of patients and suggest insights into disease dynamics to identify patients with worse prognosis and to improve the therapy. Analysis of high-dimensional time-to-event data in the presence of competing risks requires special modeling techniques. So far, some attempts have been made to variable selection in low- and high-dimension competing risk setting using partial likelihood-based procedures. In this paper, a weighted likelihood-based penalized approach is extended for direct variable selection under the subdistribution hazards model for high-dimensional competing risk data. The proposed method which considers a larger class of semiparametric regression models for the subdistribution allows for taking into account time-varying effects and is of particular importance, because the proportional hazards assumption may not be valid in general, especially in the high-dimension setting. Also, this model relaxes from the constraint of the ability to simultaneously model multiple cumulative incidence functions using the Fine and Gray approach. The performance/effectiveness of several penalties including minimax concave penalty (MCP); adaptive LASSO and smoothly clipped absolute deviation (SCAD) as well as their L₂ counterparts were investigated through simulation studies in terms of sensitivity/specificity. The results revealed that sensitivity of all penalties were comparable, but the MCP and MCP-L₂ penalties outperformed the other methods in term of selecting less noninformative variables. The practical use of the model was investigated through the analysis of genomic competing risk data obtained from patients with bladder cancer and six genes of CDC20, NCF2, SMARCAD1, RTN4, ETFDH, and SON were identified using all the methods and were significantly correlated with the subdistribution.

MethCORR infers gene expression from DNA methylation and allows molecular analysis of ten common cancer types using fresh-frozen and formalin-fixed paraffin-embedded tumor samples

Background

Transcriptional analysis is widely used to study the molecular biology of cancer and hold great biomarker potential for clinical patient stratification. Yet, accurate transcriptional profiling requires RNA of a high quality, which often cannot be retrieved from formalin-fixed, paraffin-embedded (FFPE) tumor tissue that is routinely collected and archived in clinical departments. To overcome this roadblock to clinical testing, we previously developed MethCORR, a method that infers gene expression from DNA methylation data, which is robustly retrieved from FFPE tissue. MethCORR was originally developed for colorectal cancer and with this study, we aim to: (1) extend the MethCORR method to 10 additional cancer types and (2) to illustrate that the inferred gene expression is accurate and clinically informative.

Results

Regression models to infer gene expression information from DNA methylation were developed for ten common cancer types using matched RNA sequencing and DNA methylation profiles (HumanMethylation450 BeadChip) from The Cancer Genome Atlas Project. Robust and accurate gene expression profiles were inferred for all cancer types: on average, the expression of 11,000 genes was modeled with good accuracy and an intra-sample correlation of R² = 0.90 between inferred and measured gene expression was observed. Molecular pathway analysis and transcriptional subtyping were performed for breast, prostate, and lung cancer samples to illustrate the general usability of the inferred gene expression profiles: overall, a high correlation of r = 0.96 (Pearson) in pathway enrichment scores and a 76% correspondence in molecular subtype calls were observed when using measured and inferred gene expression as input. Finally, inferred expression from FFPE tissue correlated better with RNA sequencing data from matched fresh-frozen tissue than did RNA sequencing data from FFPE tissue (P < 0.0001; Wilcoxon rank-sum test).

Conclusions

In all cancers investigated, MethCORR enabled DNA methylation-based transcriptional analysis, thus enabling future analysis of cancer in situations where high-quality DNA, but not RNA, is available. Here, we provide the framework and resources for MethCORR modeling of ten common cancer types, thereby widely expanding the possibilities for transcriptional studies of archival FFPE material.

MIXnorm: normalizing RNA-seq data from formalin-fixed paraffin-embedded samples

MOTIVATION

Recent studies have shown that RNA-sequencing (RNA-seq) can be used to measure mRNA of sufficient quality extracted from formalin-fixed paraffin-embedded (FFPE) tissues to provide whole-genome transcriptome analysis. However, little attention has been given to the normalization of FFPE RNA-seq data, a key step that adjusts for unwanted biological and technical effects that can bias the signal of interest. Existing methods, developed based on fresh-frozen or similar-type samples, may cause suboptimal performance.

RESULTS

We proposed a new normalization method, labeled MIXnorm, for FFPE RNA-seq data. MIXnorm relies on a two-component mixture model, which models non-expressed genes by zero-inflated Poisson distributions and models expressed genes by truncated normal distributions. To obtain maximum likelihood estimates, we developed a nested EM algorithm, in which closed-form updates are available in each iteration. By eliminating the need for numerical optimization in the M-step, the algorithm is easy to implement and computationally efficient. We evaluated MIXnorm through simulations and cancer studies. MIXnorm makes a significant improvement over commonly used methods for RNA-seq expression data.

AVAILABILITY AND IMPLEMENTATION

R code available at https://github.com/S-YIN/MIXnorm.

CONTACT

swang@smu.edu.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Biomarkers predicting oncological outcomes of high-risk non-muscle-invasive bladder cancer

INTRODUCTION

The European Organization for Research and Treatment of Cancer (EORTC) and the Spanish Urological Club for Oncological Treatment (CUETO) scoring systems show limited accuracy for the prediction of disease recurrence and progression of non-muscle-invasive bladder cancer (NMIBC). This aspect is even more relevant in the category of HR NMIBC. Biomarkers might potentially help to further categorize the outcomes of these patients. Therefore, we sought to review the evidence available on tissue-based, urinary, and serum biomarkers for the prediction of recurrence, progression, and survival in HR NMIBC.

EVIDENCE ACQUISITION

A systematic literature review without time restrictions was performed using PubMed/EMBASE, Web of Science, SCOPUS, and the Cochrane Libraries. The search was filtered for articles in the English, Italian, German, French, and Spanish languages, involving patients with more than 18 years of age. Relevant papers on tissue-based, serum and urinary biomarkers related to the prediction of oncological outcomes for high-risk bladder cancer patients were included in the analyses.

EVIDENCE SYNTHESIS

Overall, 71 studies were eligible for inclusion in this review. The majority of the investigations performed so far focused on immunohistochemical analyses on tumoral tissue. Overall, p53 was the most studied biomarker, but results regarding its prognostic and predictive role were contradictory. Ki67 seems to be a promising biomarker in the prediction of recurrence. Recently, PD-L1 has been associated with the prediction of recurrence free survival and of treatment-refractory disease. Markers developed un urine samples are focused on commercially available kits, which currently do not unequivocally show strongly superior levels of accuracy to cytology. However, they have demonstrated to be potentially helpful in the prediction of recurrence. Blood-based biomarkers represent an emerging reality with promising future applications.

CONCLUSIONS

Despite a long history of attempts to discover accurate biomarkers predicting oncological outcomes for HR NMIBC, contradictory or uncertain findings render the adoption of this ancillary techniques in clinical practice still unlikely. Future attempts should be directed to the development of prospective trials and the definition of standardized cut-off levels to render findings worthy of comparison.

Transcription levels and prognostic significance of the NFI family members in human cancers

Background

The nuclear factor I (NFI) is a family of transcription factors consisting of four distinct but closely related genes, NFIA, NFIB, NFIC and NFIX, which are important in the development of various tissues and organs in mammals. Recent study results have shown that NFI family may play a critical role in the progression of various human tumors and have been identified as key tumor suppressors and oncogenes for many cancers. However, the expression levels and distinctive prognostic values of the NFI family remain poorly explored in most cancers.

Materials and Methods

In the present study, the differences in mRNA expression of the NFI family in various cancers were investigated using the Oncomine and TCGA databases, and the mRNA expression, genetic alteration and DNA methylation of the NFI family members in various cancers were examined using cBioPortal for Cancer Genomics. In addition, the prognostic significance of the NFI family was assessed in multiple cancers using the Kaplan–Meier plotter (KM plotter) and SurvExpress databases.

Results

The mRNA expression levels in the NFI family were significantly downregulated in most cancers compared with normal tissues and DNA hypermethylation might downregulate the NFI family expression. Although NFIX expression was not downregulated in kidney, colorectal and prostate cancers. Furthermore, NFIB expression was upregulated in gastric cancer. Further survival analyses based on the KM plotter and SurvExpress databases showed dysregulations of the NFI genes were significantly correlated with survival outcomes in breast, lung, and head and neck cancers. Decreased expression levels of NFIA, NFIB and NFIC were associated with poor overall survival (OS) in head and neck cancer. Low mRNA expression of NFIA and NFIB was significantly associated with OS and first progression in lung adenocarcinoma, but not in lung squamous cell carcinoma. In addition, potential correlations between NFI family members and survival outcomes were also observed in liver, esophageal, kidney and cervical cancer.

Conclusion

The results from the present study indicated certain members of the NFI family could be promising therapeutic targets and novel prognostic biomarkers for human cancers.

RNA-seq from archival FFPE breast cancer samples: molecular pathway fidelity and novel discovery

BACKGROUND

Formalin-fixed, paraffin-embedded (FFPE) tissues for RNA-seq have advantages over fresh frozen tissue including abundance and availability, connection to rich clinical data, and association with patient outcomes. However, FFPE-derived RNA is highly degraded and chemically modified, which impacts its utility as a faithful source for biological inquiry.

METHODS

True archival FFPE breast cancer cases (n = 58), stored at room temperature for 2-23 years, were utilized to identify key steps in tissue selection, RNA isolation, and library choice. Gene expression fidelity was evaluated by comparing FFPE data to public data obtained from fresh tissues, and by employing single-gene, gene set and transcription network-based regulon analyses.

RESULTS

We report a single 10 μm section of breast tissue yields sufficient RNA for RNA-seq, and a relationship between RNA quality and block age that was not linear. We find single-gene analysis is limiting with FFPE tissues, while targeted gene set approaches effectively distinguish ER+ from ER- breast cancers. Novel utilization of regulon analysis identified the transcription factor KDM4B to associate with ER+ disease, with KDM4B regulon activity and gene expression having prognostic significance in an independent cohort of ER+ cases.

CONCLUSION

Our results, which outline a robust FFPE-RNA-seq pipeline for broad use, support utilizing FFPE tissues to address key questions in the breast cancer field, including the delineation between indolent and life-threatening disease, biological stratification and molecular mechanisms of treatment resistance.

Beyond BCG: the approaching era of personalised bladder-sparing therapies for non-muscle-invasive urothelial cancers

Progress in the management of non-muscle invasive bladder cancer has been slow. Despite longstanding use of intravesical therapies (e.g., Bacille Calmette-Guerin; BCG) to complement cystoscopic resection of high-grade lesions, many patients still develop recurrences requiring cystectomy, while others suffer side-effects of BCG without definite benefit. Many questions remain: for example, how many patients receive intravesical prophylaxis without efficacy? Which high-risk patients are best managed with early cystectomy? Could systemic therapies and/or radiotherapy extend bladder preservation times? Such questions may soon be refined by clinicopathologic non-muscle invasive bladder cancer signatures that predict sensitivity to cytotoxic, immune and targeted therapies. Hypothesis-based trials using these signatures should lead to more rational adjuvant treatments, longer bladder preservation times, and better quality of life for patients.

Biological Validation of RNA Sequencing Data from Formalin-Fixed Paraffin-Embedded Primary Melanomas

PURPOSE

Initiatives such as The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) have generated high-quality, multi-platform molecular data from thousands of frozen tumor samples. While these initiatives have provided invaluable insight into cancer biology, a tremendous potential resource remains largely untapped in formalin-fixed, paraffin-embedded (FFPE) samples that are more readily available, but which can present technical challenges due to crosslinking of fragile molecules such as RNA.

MATERIALS AND METHODS

We extracted RNA from FFPE primary melanomas and assessed two gene expression platforms -- genome-wide RNA sequencing (RNA-seq) and targeted NanoString -- for their ability to generate coherent biological signals. To do so, we generated an improved approach to quantifying gene expression pathways, in which we refine pathway scores through correlation-guided gene subsetting. We also make comparisons to the TCGA and other publicly available melanoma datasets.

RESULTS

Comparison of the gene expression patterns to each other, to established biological modules, and to clinical and immunohistochemical data confirmed the fidelity of biological signals from both platforms using FFPE samples to known biology. Moreover, correlations with patient outcome data were consistent with previous frozen-tissue-based studies.

CONCLUSION

FFPE samples from previously difficult-to-access cancer types - such as small primary melanomas - represents a valuable and previously unexploited source of analyte for RNA-seq and NanoString platforms. This work provides an important step towards the use of such platforms to unlock novel molecular underpinnings and inform future biologically-driven clinical decisions.

Molecular Progression Risk Score for Prediction of Muscle Invasion in Primary T1 High-Grade Bladder Cancer

BACKGROUND

Pathologic T1 high-grade (pT1HG) bladder cancer (BC) is characterized by a high progression rate and constitutes an important clinical challenge; however, there is no consensus on the prediction of progression in pT1HG BC. The purpose of this study was to validate previously published molecular progression risk score (MoPRS) for predicting muscle-invasive disease in pT1HG BC.

MATERIALS AND METHODS

The expression of an 8-gene progression-related classifier identified from microarray data was analyzed by real-time PCR, and the MoPRS was calculated in 121 newly recruited patients with pT1HG BC. Progression was defined as muscle invasion or metastasis.

RESULTS

Overall, the disease of 28 patients (23.1%) progressed to muscle-invasive BC during the median follow-up of 63.7 (interquartile range, 17.6-96.4) months. The MoPRS was significantly higher in 1973 World Health Organization grade 3 than grade 2 tumors (P = .004). Early development of invasive BC was more prevalent in the highest quartile MoPRS group than in the lowest to 75th percentile MoPRS groups according to Kaplan-Meier analysis. Multivariate Cox regression analysis revealed that the MoPRS was an independent predictor of invasive BC, either as a continuous variable (hazard ratio, 1.624; 95% confidence interval, 1.266-2.082; P < .001) or as a categorical variable (hazard ratio, 3.089; 95% confidence interval, 1.335-7.150; P = .008).

CONCLUSION

The MoPRS was an independent prognostic factor for identifying patients at high risk of invasive BC in patients with pT1HG BC. This scale may help identify patients who could benefit from more aggressive therapeutic intervention such as early cystectomy.

The convergent roles of the nuclear factor I transcription factors in development and cancer

The nuclear factor I (NFI) transcription factors play important roles during normal development and have been associated with developmental abnormalities in humans. All four family members, NFIA, NFIB, NFIC and NFIX, have a homologous DNA binding domain and function by regulating cell proliferation and differentiation via the transcriptional control of their target genes. More recently, NFI genes have also been implicated in cancer based on genomic analyses and studies of animal models in a variety of tumours across multiple organ systems. However, the association between their functions in development and in cancer is not well described. In this review, we summarise the evidence suggesting a converging role for the NFI genes in development and cancer. Our review includes all cancer types in which the NFI genes are implicated, focusing predominantly on studies demonstrating their oncogenic or tumour-suppressive potential. We conclude by presenting the challenges impeding our understanding of NFI function in cancer biology, and demonstrate how a developmental perspective may contribute towards overcoming such hurdles.

The Utilization of Formalin Fixed-Paraffin-Embedded Specimens in High Throughput Genomic Studies

High throughput genomic assays empower us to study the entire human genome in short time with reasonable cost. Formalin fixed-paraffin-embedded (FFPE) tissue processing remains the most economical approach for longitudinal tissue specimen storage. Therefore, the ability to apply high throughput genomic applications to FFPE specimens can expand clinical assays and discovery. Many studies have measured the accuracy and repeatability of data generated from FFPE specimens using high throughput genomic assays. Together, these studies demonstrate feasibility and provide crucial guidance for future studies using FFPE specimens. Here, we summarize the findings of these studies and discuss the limitations of high throughput data generated from FFPE specimens across several platforms that include microarray, high throughput sequencing, and NanoString.

Competing risks data analysis with high-dimensional covariates: an application in bladder cancer

Analysis of microarray data is associated with the methodological problems of high dimension and small sample size. Various methods have been used for variable selection in high-dimension and small sample size cases with a single survival endpoint. However, little effort has been directed toward addressing competing risks where there is more than one failure risks. This study compared three typical variable selection techniques including Lasso, elastic net, and likelihood-based boosting for high-dimensional time-to-event data with competing risks. The performance of these methods was evaluated via a simulation study by analyzing a real dataset related to bladder cancer patients using time-dependent receiver operator characteristic (ROC) curve and bootstrap .632+ prediction error curves. The elastic net penalization method was shown to outperform Lasso and boosting. Based on the elastic net, 33 genes out of 1381 genes related to bladder cancer were selected. By fitting to the Fine and Gray model, eight genes were highly significant (P<0.001). Among them, expression of RTN4, SON, IGF1R, SNRPE, PTGR1, PLEK, and ETFDH was associated with a decrease in survival time, whereas SMARCAD1 expression was associated with an increase in survival time. This study indicates that the elastic net has a higher capacity than the Lasso and boosting for the prediction of survival time in bladder cancer patients. Moreover, genes selected by all methods improved the predictive power of the model based on only clinical variables, indicating the value of information contained in the microarray features.

The analytical landscape of static and temporal dynamics in transcriptome data

Interpreting gene expression profiles often involves statistical analysis of large numbers of differentially expressed genes, isoforms, and alternative splicing events at either static or dynamic spectrums. Reduced sequencing costs have made feasible dense time-series analysis of gene expression using RNA-seq; however, statistical methods in the context of temporal RNA-seq data are poorly developed. Here we will review current methods for identifying temporal changes in gene expression using RNA-seq, which are limited to static pairwise comparisons of time points and which fail to account for temporal dependencies in gene expression patterns. We also review recently developed very few number of temporal dynamic RNA-seq specific methods. Application and development of RNA-specific temporal dynamic methods have been continuously under the development, yet, it is still in infancy. We fully cover microarray specific temporal methods and transcriptome studies in initial digital technology (e.g., SAGE) between traditional microarray and new RNA-seq.